42 citations · 91 across the 11 of their papers we have counts for
11 papers
Parrot-Trained Adversarial Examples: Pushing the Practicality of Black-Box Audio Attacks against Speaker Recognition Models
Rui Duan, Zhe Qu, Leah Ding +2
Audio adversarial examples (AEs) have posed significant security challenges to real-world speaker recognition systems. Most black-box attacks still require certain information from…
Data-Driven Next-Generation Wireless Networking: Embracing AI for Performance and Security
Jiahao Xue, Zhe Qu, Shangqing Zhao +2
New network architectures, such as the Internet of Things (IoT), 5G, and next-generation (NextG) cellular systems, put forward emerging challenges to the design of future wireless…
Modeling Global Distribution for Federated Learning with Label Distribution Skew
Tao Sheng, Chengchao Shen, Yuan Liu +3
Federated learning achieves joint training of deep models by connecting decentralized data sources, which can significantly mitigate the risk of privacy leakage. However, in a more…
On the Convergence of Multi-Server Federated Learning with Overlapping Area
Zhe Qu, Xingyu Li, Jie Xu +3
Multi-server Federated learning (FL) has been considered as a promising solution to address the limited communication resource problem of single-server FL. We consider a typical mu…
Perception-Aware Attack: Creating Adversarial Music via Reverse-Engineering Human Perception
Rui Duan, Zhe Qu, Shangqing Zhao +3
Recently, adversarial machine learning attacks have posed serious security threats against practical audio signal classification systems, including speech recognition, speaker reco…
Generalized Federated Learning via Sharpness Aware Minimization
Zhe Qu, Xingyu Li, Rui Duan +3
Federated Learning (FL) is a promising framework for performing privacy-preserving, distributed learning with a set of clients. However, the data distribution among clients often e…
LoMar: A Local Defense Against Poisoning Attack on Federated Learning
Xingyu Li, Zhe Qu, Shangqing Zhao +3
Federated learning (FL) provides a high efficient decentralized machine learning framework, where the training data remains distributed at remote clients in a network. Though FL en…
Context-Aware Online Client Selection for Hierarchical Federated Learning
Zhe Qu, Rui Duan, Lixing Chen +3
Federated Learning (FL) has been considered as an appealing framework to tackle data privacy issues of mobile devices compared to conventional Machine Learning (ML). Using Edge Ser…
FedLGA: Towards System-Heterogeneity of Federated Learning via Local Gradient Approximation
Xingyu Li, Zhe Qu, Bo Tang +1
Federated Learning (FL) is a decentralized machine learning architecture, which leverages a large number of remote devices to learn a joint model with distributed training data. Ho…
How to Test the Randomness from the Wireless Channel for Security?
Zhe Qu, Shangqing Zhao, Jie Xu +2
We revisit the traditional framework of wireless secret key generation, where two parties leverage the wireless channel randomness to establish a secret key. The essence in the fra…
Stragglers Are Not Disaster: A Hybrid Federated Learning Algorithm with Delayed Gradients
Xingyu Li, Zhe Qu, Bo Tang +1
Federated learning (FL) is a new machine learning framework which trains a joint model across a large amount of decentralized computing devices. Existing methods, e.g., Federated A…